Title

Sieve Estimation of Panel Data Models with Cross Section Dependence

Publication Type

Journal Article

Publication Date

2012

Abstract

In this paper we consider the problem of estimating semiparametric panel data models with cross section dependence, where the individual-specific regressors enter the model nonparametrically whereas the common factors enter the model linearly. We consider both heterogeneous and homogeneous regression relationships when both the time and cross-section dimensions are large. We propose sieve estimators for the nonparametric regression functions by extending Pesaran’s (2006) common correlated effect (CCE) estimator to our semiparametric framework. Asymptotic normal distributions for the proposed estimators are derived and asymptotic variance estimators are provided. Monte Carlo simulations indicate that our estimators perform well in finite samples.

Keywords

Common factor, Cross-section dependence, Heterogeneous regression, Panel data, Sieve estimation

Discipline

Econometrics

Research Areas

Econometrics

Publication

Journal of Econometrics

Volume

169

Issue

1

First Page

34

Last Page

47

ISSN

0304-4076

Identifier

10.1016/j.jeconom.2012.01.006

Publisher

Elsevier

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://dx.doi.org/10.1016/j.jeconom.2012.01.006

This document is currently not available here.

Share

COinS